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The Takagi-Sugeno (TS) fuzzy rule system is a widely used data mining technique, and is of particular use in the identification of non-linear interactions between variables. However the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). Few robust methods are available to identify important rules while removing redundant ones, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. Here, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data.

Although inequalities in health and socioeconomic status have an important influence on childhood educational performance, the interactions between these multiple factors relating to variation in educational outcomes at micro-level is unknown, and how to evaluate the many possible interactions of these factors is not well established. This paper aims to examine multi-dimensional deprivation factors and their impact on childhood educational outcomes at micro-level, focusing on geographic areas having widely different disparity patterns, in which each area is characterised by six deprivation domains (Income, Health, Geographical Access to Services, Housing, Physical Environment, and Community Safety). Traditional health statistical studies tend to use one global model to describe the whole population for macro-analysis. In this paper, we combine linked educational and deprivation data across small areas (median population of 1500), then use a local modelling technique, the Takagi-Sugeno fuzzy system, to predict area educational outcomes at ages 7 and 11. We define two new metrics, “Micro-impact of Domain” and “Contribution of Domain”, to quantify the variations of local impacts of multidimensional factors on educational outcomes across small areas. The two metrics highlight differing priorities. Our study reveals complex multi-way interactions between the deprivation domains, which could not be provided by traditional health statistical methods based on single global model. We demonstrate that although Income has an expected central role, all domains contribute, and in some areas Health, Environment, Access to Services, Housing and Community Safety each could be the dominant factor. Thus the relative importance of health and socioeconomic factors varies considerably for different areas, depending on the levels of each of the other factors, and therefore each component of deprivation must be considered as part of a wider system. Childhood educational achievement could benefit from policies and intervention strategies that are tailored to the local geographic areas' profiles. [ABSTRACT FROM AUTHOR]

The relationship between toxic marine microalgae species and climate change has become a high profile and well discussed topic in recent years, with research focusing on the possible future impacts of changing hydrological conditions on Harmful Algal Bloom (HAB) species around the world. However, there is very little literature concerning the epidemiology of these species on marine organisms and human health. Here, we examine the current state of toxic microalgae species around the UK, in two ways: first we describe the key toxic syndromes and gather together the disparate reported data on their epidemiology from UK records and monitoring procedures. Secondly, using NHS hospital admissions and GP records from Wales, we attempt to quantify the incidence of shellfish poisoning from an independent source. We show that within the UK, outbreaks of shellfish poisoning are rare but occurring on a yearly basis in different regions and affecting a diverse range of molluscan shellfish and other marine organisms. We also show that the abundance of a species does not necessarily correlate to the rate of toxic events. Based on routine hospital records, the numbers of shellfish poisonings in the UK are very low, but the identification of the toxin involved, or even a confirmation of a poisoning event is extremely difficult to diagnose. An effective shellfish monitoring system, which shuts down aquaculture sites when toxins exceed regularity limits, has clearly prevented serious impact to human health, and remains the only viable means of monitoring the potential threat to human health. However, the closure of these sites has an adverse economic impact, and the monitoring system does not include all toxic plankton. The possible geographic spreading of toxic microalgae species is therefore a concern, as warmer waters in the Atlantic could suit several species with southern biogeographical affinities enabling them to occupy the coastal regions of the UK, but which are not yet monitored or considered to be detrimental. [ABSTRACT FROM AUTHOR]

Introduction: Statin treatment reduces cardiovascular morbidity and mortality in those with and at risk of cardiovascular disease (CVD) and is endorsed in all major clinical guidelines for the prevention of CVD. Despite the weight of high quality evidence supporting benefits of treatment versus relatively rare serious side effects, the use of statins remains variable in clinical practice with high discontinuation rates reported for a treatment intended to be life-long. Our objectives were to determine (i) the rate of discontinuation of statin prescription (attrition) post-PCI, (ii) clinical factors associated with attrition, (iii) the risk of adverse events associated with attrition. Methods: We studied 3,478 patients (2011-2105) who were prescribed a statin post-Percutaneous Coronary Intervention (PCI) within a retrospective observational cohort using linked anonymised primary care and hospital health record data, including any record of ongoing statin prescription. Relationships between attrition and major clinical outcomes (death, acute coronary syndromes, revascularisation or stroke) were evaluated over 18-month follow up. Results: Cumulative attrition of statin prescription in the first six quarters post-PCI was 2.4%, 4.7%, 6.6%, 8.3%, 9.6% and 11.2% respectively. Heart failure and age ≥70y were independent predictors of attrition. 587 (16.9%) patients had an outcome-event during the follow up period. In a time-dependent multiple Cox regression model, statin attrition (HR=6.65, p <0.001), hypertension (HR=1.29, p=0.004), diabetes (HR=1.27, p= 0.01), previous MI (HR=1.42, p <0.001), ischaemic stroke (HR=1.42 p=0.005), thromboembolism (HR= 1.89, p= 0.038), Atrial Fibrillation (HR=1.30, p=0.018) and age ≤49 (HR=1.73, p=0.002) and age ≥70 (HR=1.88, p <0.001) vs reference age 50-59y were independently associated with adverse clinical outcomes. Conclusions: Attrition from statin therapy is lower than previously reported in the first 18-months post-PCI. However, despite this low rate of statin attrition, it is still independently associated with a high risk of adverse outcomes. [ABSTRACT FROM AUTHOR]